Digital image analysis of fluid inclusions

Xu, Jian; Zhao, Xiaobo; Liu, Bin

doi:10.1016/j.ijrmms.2007.01.003

Cited by 11 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the DIP method, the microstructures of geomaterials can be identified accurately within a certain resolution. As a result, DIP has been widely applied in the detection of morphological characteristics for geomaterials (Ammouche et al, 2000(Ammouche et al, , 2001Obaidat et al, 1998;Xu et al, 2007a;Yue et al, 1995;Yue and Morin, 1996). As problems of heterogeneous media are so complex that they cannot be solved analytically, further studies have been made to incorporate the DIP technique into numerical methods (Yue et al, 2003(Yue et al, , 2004Armesto et al, 2009;Chen et al, 2004Chen et al, , 2005Chen et al, , 2007Michailidis et al, 2010;Xu et al, 2007Xu et al, , 2008aXu et al, , 2008b.…”

Section: Introductionmentioning

confidence: 99%

A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials

Meng

Wang

et al. 2018

View full text Add to dashboard Cite

Purpose This paper aims to present a digital image processing (DIP)-based discrete element method (DEM) for the analysis of heterogeneous geomaterials. Taking a soil and rock mixture as an example, the direct shear test is used to illustrate the application of this method. The numerical result is validated by the laboratory experiment and implies its feasibility in the analysis of heterogeneous geomaterials. Design/methodology/approach This method has two major steps. Based on a modification of the connected-component labeling algorithm, a novel vectorization method, which can transform the digital photos to vectorized geometry automatically, is proposed first. Then, a simple yet effective method for the generation of heterogeneous DEM models is presented using the simulation of simplicity technique. Findings DIP-DEM method is a feasible approach for the analysis of mechanical behavior of heterogeneous material. For soil and rock mixtures (SRM), the horizantal deformation at peak shear point becomes larger with the normal stress. Compared with pure soil, the rock aggregates mainly improve the friction angle of SRM. Originality/value As a universal method taking advantage of both DIP and DEM, this method has broad application prospects in related fields.

show abstract

Section: Introductionmentioning

confidence: 99%

A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials

Meng

Wang

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The classification algorithms based on Sobel, Marr-Hildreth, Harr wavelet and texture analyses were respectively applied to classify the damage and non-damage of rock fracture structures, which verified that the algorithm, based on texture analysis, could reach to the best results [7]. The fracture detection on coal surfaces and the estimation of fluid inclusion traces were conducted but the precisions of both detection and regression were to be further improved [8]. The fractures on 3-D were enhanced by the multi-scale filter based on Hessian matrix, which gained the satisfactory results but was impacted by selected scales [9].…”

Section: Introductionmentioning

confidence: 84%

Rock Fracture Centerline Extraction based on Hessian Matrix and Steger Algorithm

2015

KSII TIIS

View full text Add to dashboard Cite

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

show abstract

“…Obviously, without using any special technique of automatic detection, the solution of this problem is not only technically complex but is also time consuming. Many works present results of successful automatic lineament detection at microscales [27,[58][59][60], mesoscales [61,62] and macroscales [61][62][63]. The development of digital technologies allows taking a fresh look at microstructural analyses.…”

Section: Introductionmentioning

confidence: 99%

Prospects of Geoinformatics in Analyzing Spatial Heterogeneities of Microstructural Properties of a Tectonic Fault

et al. 2022

View full text Add to dashboard Cite

The paper proposes a special technique for microstructural analysis (STMA) of rock samples based on two provisions. The first one is an algorithm for the automatic detection and digitalization of microstructures in images of oriented thin sections. The second one utilizes geographic information system (GIS) tools for an automatized analysis of objects at the micro scale. Using STMA allows the establishment of geometric features of fissure and pore space of rock samples to determine the parameters of stress–strain fields at different stages of rock massif deformation and to establish a relationship between microstructures and macrostructures. STMA makes it possible to evaluate the spatial heterogeneity of physical and structural properties of rocks at the micro scale. Verification of STMA was carried out using 15 rock samples collected across the core of the Primorsky Fault of the Baikal Rift Zone. Petrographic data were compared to the quantitative parameters of microfracture networks. The damage zone of the Primorsky Fault includes three clusters characterized by different porosity, permeability, and deformation type. Findings point to the efficiency of STMA in revealing the spatial heterogeneity of a tectonic fault.

show abstract

Digital image analysis of fluid inclusions

Cited by 11 publications

References 14 publications

A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials

A coupling method incorporating digital image processing and discrete element method for modeling of geomaterials

Rock Fracture Centerline Extraction based on Hessian Matrix and Steger Algorithm

Prospects of Geoinformatics in Analyzing Spatial Heterogeneities of Microstructural Properties of a Tectonic Fault

Contact Info

Product

Resources

About